Discriminative Embeddings of Latent Variable Models for Structured Data

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• Prof Le Song (Georgia Tech)
• Monday 25 July 2016, 11:00-12:00
• Engineering Department, CBL Room BE-438.

If you have a question about this talk, please contact Zoubin Ghahramani.

Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design. Typically, kernels are designed beforehand for a data type which either exploit statistics of the structures or make use of probabilistic generative models, and then a discriminative classifier is learned based on the kernels via convex optimization. However, such an elegant two-stage approach also limited kernel methods from scaling up to millions of data points, and exploiting discriminative information to learn feature representations.

In this talk, I will present structure2vec, an effective and scalable approach for structured data representation based on the idea of embedding latent variable models into feature spaces, and learning such feature spaces using discriminative information. Interestingly, structure2vec extracts features by performing a sequence of function mappings in a way similar to graphical model inference procedures, such as the mean field and belief propagation algorithm. In applications involving millions of data points, we showed that structure2vec runs 2 times faster, produces models which are 10, 000 times smaller, while at the same time achieving the state-of-the-art predictive performance.

Bio: Le Song is an assistant professor in the Department of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. He received his Ph.D. in Machine Learning from University of Sydney and NICTA in 2008, and then conducted his post-doctoral research in the Department of Machine Learning, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology, he was a research scientist at Google. His principal research direction is machine learning, especially nonlinear methods and probabilistic graphical models for large scale and complex problems, arising from artificial intelligence, social network analysis, healthcare analytics, and other interdisciplinary domains. He is the recipient of the NSF CAREER Award’14, AISTATS ’16 Best Student Paper Award, IPDPS ’15 Best Paper Award, NIPS ’13 Outstanding Paper Award, and ICML ’10 Best Paper Award. He has also served as the area chair for leading machine learning conferences such as ICML , NIPS and AISTATS , and the action editor for JMLR .

This talk is part of the Machine Learning @ CUED series.

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